A picture is worth a thousand words

Data visualisations are demonstrably useful

There is considerable experimental evidence for data visualisations improving:

• Comprehension of data

• Decision making accuracy and confidence

Evidence has been collected using eye-tracking, survey filling and interviews.

For a good overview of the available research see Eberhard 2021¹.

Some of these studies consider tables to be a type of data visualisation.

I agree with this! Tables are often awesome choices for presenting data - let’s talk more about this later today.

Data visualisations are demonstrably useful

Data visualisations are demonstrably useful

Data visualisations are demonstrably useful

Data visualisations are demonstrably useful

Data visualisations are demonstrably useful

Data visualisations are demonstrably useful

Data visualisations are demonstrably useful

Evidence-based visual perception theory

There is a wealth of evidence-based research in how precisely or accurately charts are perceived by readers.

Source: Wikimedia.org

Our evidence comes from:

• Eye tracking. We’re really good at measuring where the eye is looking, for how long and how intently.

• Asking trial participants to estimate or compare values in charts.

There are open debates¹ on how our internal visual perception system works - what the brain is doing.

Elementary perceptual tasks

Elementary perceptual tasks

Crowd-sourced evidence for perception theory

Ordering channels of communication (by accuracy)

Images from Beecham et al¹³

… real-world applications of visual perception theory (I)

Images from Robert Kosara¹⁴

… real-world applications of visual perception theory (II)

Images from Robert Kosara¹⁴

… real-world applications of visual perception theory (III)

Image found on Twitter from @irg_bio¹⁵ - code for chart available from GitHub¹⁶.

Why is someone reading measuring your chart?

Why is someone reading your chart?

data-to-viz.com

This site also provides simple to follow instructions for using {ggplot2} to build every single chart type you can find on the website.

ft-interactive.github.io/visual-vocabulary

The Visual Vocabulary is a really useful tool for thinking about how to tell your story with a chart.

Lots of the dataviz at the FT is done with R. John Burn-Murdoch¹⁷ is a great source to follow.

📝 Task: Setup a new project

SLIDE 1 OF 3

1. Create a new project called something like week-4_dataviz.Rproj

2. Add a new RMarkdown document called ggplot2-notes.Rmd

We’re going to do some structured and unstructured code during today. During the workshop I’ll be asking to you to create your own charts.

ggplot2: A Grammar of Graphics

{ggplot2} is an incredibly powerful and flexible tool for building static dataviz.

We can build (almost)¹ any static chart we can conceive of.

[1] - Dual y-axis charts must be transformations of one another (for good reasons)

Some geoms are built from others (I)

ggplot(quakes, aes(mag)) +
  geom_histogram()

ggplot(quakes, aes(mag)) +
  geom_bar() +
  scale_x_binned()

Some geoms are built from others (II)

But geom_bar() itself is built from geom_rect().

rect_data <- tribble(
  ~x_min, ~x_max, ~y_min, ~y_max,
  4, 4.48, 0, 60,
  4.5, 5.48, 0, 100,
  5.5, 5.98, 0, 10
)
rect_data %>% 
  ggplot() +
  geom_rect(aes(xmin = x_min, 
                xmax = x_max, 
                ymin = y_min, 
                ymax = y_max)) +
    theme_gray(base_size = 25)

There are 8 primitives from which all other geoms are built:

geom_blank(), geom_path(), geom_point(), geom_polygon(), geom_rect(), geom_ribbon(), geom_segment(), geom_text()

All geoms have x and y aesthetics

These tell the geom where it needs to be drawn:

starwars %>% 
  ggplot() +
  aes(x = height,
      y = mass) +
  geom_point()

Some geoms need more than just x and y

Let’s geom_segment() to visualise some of the eras of the dinosaurs:

dinosaurs <- tribble(
  ~period, ~start, ~end,
  "Triassic Period", -251e6, -225e6,
  "Late Triassic Period", -225e6, -200e6,
  "Jurassic Period", -200e6, -150e6,
  "Late Jurassic Period", -150e6, -145e6
)

To build this chart we need to specify all of the following: x, xend, y and yend.

Use size to affect geom size

In many charts we want geoms to be thicker, bigger or just be more prominent.

Timeline (or Gantt charts) are good examples of this. We want the segments to be thicker to improve the readability of the chart - this comes down to the size aesthetic.

dinosaurs %>% 
  ggplot() +
  aes(x = start, xend = end,
      y = period, yend = period) +
  geom_segment(size = 30)

Out of order dinosaurs

This is still a bad chart.

The eras are not ordered in geological time, instead they’re ordered (reverse) alphabetically.

To control the order of things in {ggplot2} charts we must use factors - which are picked up by the scales.

Some geoms are designed to save time

mpg %>% 
  count(manufacturer) %>% 
  ggplot() +
  geom_bar(aes(manufacturer))

mpg %>% 
  count(manufacturer) %>% 
  ggplot() +
  geom_col(aes(x = manufacturer, y = n))

Some geoms depend on stat functions

The geom_bar() function has a stat argument with the default value of "count".

We can force the geom to behave like geom_col() by changing the stat:

mpg %>% 
  count(manufacturer) %>% 
  ggplot() +
  geom_bar(aes(x = manufacturer,
               y = n),
           stat = "identity")

All of the goodness from the stat argument comes from the stat_identity() and stat_count() functions.

If you’re building a complex chart it might be useful to directly call a stat_() function.

Position things to resolve overlapping (I)

Box and whisker diagrams hide a lot of detail

bechdel %>% 
  filter(complete.cases(.),
         domgross_2013 < 0.5e9) %>% 
  ggplot(aes(clean_test, 
             domgross_2013)) +
  geom_boxplot() +
  theme_gray(base_size = 25)

Let’s add the data points to this chart with geom_point() and look at the position argument.

Position things to resolve overlapping (II)

The position argument can also be used to create three different types of bar chart:

• “stack” creates a stacked bar chart

• “fill” creates a proportional bar chart

• “dodge” creates a grouped bar chart

Let’s create all 3 of these for the following dataset:

gss_cat %>% 
  count(relig, marital)

# A tibble: 78 × 3
   relig      marital           n
   <fct>      <fct>         <int>
 1 No answer  No answer         4
 2 No answer  Never married    22
 3 No answer  Separated         3
 4 No answer  Divorced         13
 5 No answer  Widowed           7
 6 No answer  Married          44
 7 Don't know Never married     6
 8 Don't know Separated         3
 9 Don't know Divorced          1
10 Don't know Married           5
# … with 68 more rows

Geom layers are placed on top of one another

ggplot(mpg, 
       aes(displ, hwy)) +
  geom_point() +
  geom_smooth(method = lm, 
              formula = y ~ splines::bs(x, 3),
              size = 5)

The geom_smooth() line is hiding data points.

We could either swap the order of these geoms or change the alpha aesthetic.

Scales and {scales}

{ggplot2} uses the {scales} package under the hood to build all of the scales that we see - including continuous and discrete scales.

The {scales} package also contains many utility functions that are useful for us to format our axes and other scales.

We can either load the {scales} package itself or call functions specifically with scales::label_percent()

{scales} and deprecation (I)

Until recently the way we’d use {scales} would be as follows

percent(c(0.3, 0.5, 0.6))

[1] "30%" "50%" "60%"

msleep %>% 
  mutate(sleep_perc = sleep_total / 24,
         sleep_rem_perc = sleep_rem / 24) %>% 
  ggplot() +
  aes(x = sleep_perc,
      y = sleep_rem_perc) +
  geom_point() +
  scale_x_continuous(label = percent_format()) +
  theme_gray(base_size = 24)

There was a function called percent(x) for formatting a vector of values x and percent_format() for modifying the appearance of percentages in a {ggplot2} chart.

{scales} and deprecation (II)

These functions have now been deprecated. This means there are new alternatives to these functions.

Deprecation is a fact of life in software development. But the details of how things are deprecated are variable.

Sometimes things are deprecated with the intention of removing them in the future. Other times, the deprecated functions will continue to exist far into the future.

It seems like the intention is for these functions to continue to work into the future. But they might be removed in several years time.

Let’s use the new approach for formatting scales so that you can read modern documentation and so you’re not learning deprecated functions.

{scales} and deprecation (III)

We now use label_percent() for both types of operation.

label_percent()(c(0.3, 0.5, 0.6))

[1] "30%" "50%" "60%"

msleep %>% 
  mutate(sleep_perc = sleep_total / 24,
         sleep_rem_perc = sleep_rem / 24) %>% 
  ggplot() +
  aes(x = sleep_perc,
      y = sleep_rem_perc) +
  geom_point() +
  scale_x_continuous(label = label_percent()) +
  theme_gray(base_size = 24)

{scales} and colours (I)

There are many built-in colour palettes in {scales} - let me introduce two families of palettes.

msleep %>% 
  count(conservation) %>% 
  ggplot() +
  aes(x = n,
      y = conservation,
      fill = conservation) +
  geom_col() +
  scale_fill_brewer(palette = "Set2")

{scales} and colours (II)

There are many built-in colour palettes in {scales} - let me introduce two families of palettes.

{scales} and colours (III)

There are many built-in colour palettes in {scales} - let me introduce two families of palettes.

countries110 %>% 
  st_as_sf() %>% 
  left_join(filter(gapminder, year == 2007),
            by = c("name" = "country")) %>% 
  ggplot() +
  geom_sf(aes(fill = lifeExp)) +
  scale_fill_viridis_c()

Setting custom colours (I)

One of the first frustrations people find with {ggplot2} is setting our own custom colours, eg in this chart:

msleep %>% 
  count(vore) %>% 
  ggplot() +
  aes(x = n,
      y = vore,
      fill = ifelse(vore == "herbi", "No meat", "Some meat")) +
  geom_col()

Setting custom colours (II)

We need to use scale_fill_manual()

msleep %>% 
  count(vore) %>% 
  ggplot() +
  aes(x = n,
      y = vore,
      fill = ifelse(vore == "herbi", "No meat", "Some meat")) +
  geom_col() +
  scale_fill_manual(values = c("Some meat" = "red",
                               "No meat" = "darkgreen"))

We’ll come back to this chart in the section on guides().

{scales} and factors (I)

Factors are R’s categorical data type. They allow us to create a variable with fixed values (levels) and to set the order of those levels.

Let’s look at a pre-existing dataset with factors:

gss_cat %>% 
  head() %>% 
  pull(rincome)

[1] $8000 to 9999  $8000 to 9999  Not applicable Not applicable Not applicable
[6] $20000 - 24999
16 Levels: No answer Don't know Refused $25000 or more ... Not applicable

gss_cat %>% 
  count(rincome) %>% 
  ggplot() +
  aes(x = n,
      y = rincome) +
  geom_col() +
  theme_gray(base_size = 24)

{scales} and factors (II)

The base R tools for creating and manipulating factors are messy and frustrating to use.

We’re going to use the {forcats} package which is loaded when we run library(tidyverse).

Almost all of the functions begin with fct_*() to let you know we’re dealing with factors.

factors and msleep

Let’s think of the different ways we could order this dataset:

msleep %>% 
  count(vore)

# A tibble: 5 × 2
  vore        n
  <chr>   <int>
1 carni      19
2 herbi      32
3 insecti     5
4 omni       20
5 <NA>        7

msleep factor: count order (I)

We use fct_reorder() to order a factor by another column.

msleep %>% 
  count(vore) %>% 
  mutate(vore = fct_reorder(vore, n)) %>% 
  ggplot() +
  aes(x = n,
      y = vore) +
  geom_col() +
  theme_gray(base_size = 24)

But what about the NA values? What should we do?

msleep factor: count order (II)

We can replace NA values nicely with fct_explicit_na()

msleep %>% 
  count(vore) %>% 
  mutate(vore = fct_reorder(vore, n),
         vore = fct_explicit_na(vore, "Unknown diet")) %>% 
  ggplot() +
  aes(x = n,
      y = vore) +
  geom_col() +
  theme_gray(base_size = 24)

Let’s come back to moving the position of the NA level.

msleep factor: canonical order (I)

To set our own canonical order we use fct_relevel() and provide a vector with our preferred order.

order_vore <- c("carni", "omni", "insecti", "herbi")

msleep %>% 
  count(vore) %>% 
  mutate(vore = fct_relevel(vore, order_vore),
         vore = fct_rev(vore)) %>% 
  ggplot() +
  aes(x = n,
      y = vore) +
  geom_col() +
  theme_gray(base_size = 24)

msleep factor: canonical order (II)

We can also use fct_relevel() to modify the position of a specific

msleep %>% 
  count(vore) %>% 
  mutate(vore = fct_relevel(vore, order_vore),
         vore = fct_rev(vore),
         vore = fct_explicit_na(vore, "Unknown diet"),
         vore = fct_relevel(vore, "Unknown diet", after = 0)) %>% 
  ggplot() +
  aes(x = n,
      y = vore) +
  geom_col() +
  theme_gray(base_size = 24)

📝 Task: Global Burden of Disease factors

SLIDE 1 OF 3

These are the same steps you’ve repeated before

1. Add a sub-folder to your project called data

2. Inside of the data folder add a script called obtain-data.R

3. Add this code

download.file("https://raw.githubusercontent.com/charliejhadley/eng7218_data-science-for-healthcare-applications_bcu-masters/main/static/datasets/data-example_global-burden-of-disease/data-example_global-burden-of-disease.csv",
              destfile = "data/global-burden-of-disease-data.csv")

5. Run the code

📝 Task: Global Burden of Disease factors

SLIDE 2 OF 3

1. Add a new heading for the GBD Dataset to your .Rmd

2. Filter the dataset as follows:

• Most recent year

• location_name starts with “World Bank”

• metric_name is “Number”

• cause_name is “Injuries”

3. Select only these columns

• location_name, cause_name, val

📝 Task: Global Burden of Disease factors

SLIDE 2 OF 3

Create two versions of this chart:

• Bars are ordered by their size

• Bars are ordered from “World Bank High Income” to “World Bank Low Income”

gdb_injuries %>% 
  ggplot() +
  aes(x = val,
      y = location_name) +
  geom_col() +
  theme_gray(base_size = 24)

“Manual legends” (I)

Sometimes we want to add additional legend items - usually for NA values, and particularly for maps.

Let’s continue with this chart from before:

msleep %>% 
  count(vore) %>% 
  ggplot() +
  aes(x = n,
      y = vore,
      fill = ifelse(vore == "herbi", 
                    "No meat", 
                    "Some meat")) +
  geom_col() +
  scale_fill_manual(values = c("Some meat" = "red",
                               "No meat" = "darkgreen"),
                    name = "")

“Manual legends” (II)

We need to choose an aesthetic that works for geom_col() but we’re not using elsewhere in the chart.

This will change depending on your chart. In this instance we can use size

msleep %>% 
  count(vore) %>% 
  ggplot() +
  aes(x = n,
      y = vore,
      fill = ifelse(vore == "herbi", "No meat", "Some meat")) +
  geom_col(aes(size = "Unknown diet")) +
  scale_fill_manual(values = c("Some meat" = "red",
                               "No meat" = "darkgreen"),
                    name = "")

“Manual legends” (III)

We now set the na.value colour for the original scale_fill_manual() scale

msleep %>% 
  count(vore) %>% 
  ggplot() +
  aes(x = n,
      y = vore,
      fill = ifelse(vore == "herbi", "No meat", "Some meat")) +
  geom_col(aes(size = "Unknown diet")) +
  scale_fill_manual(values = c("Some meat" = "red",
                               "No meat" = "darkgreen"),
                    name = "",
                    na.value = "blue")

“Manual legends” (IV)

Next we use the guides() function to override the values for the size legend

msleep %>% 
  count(vore) %>% 
  ggplot() +
  aes(x = n,
      y = vore,
      fill = ifelse(vore == "herbi", "No meat", "Some meat")) +
  geom_col(aes(size = "Unknown diet")) +
  scale_fill_manual(values = c("Some meat" = "red",
                               "No meat" = "darkgreen"),
                    name = "",
                    na.value = "blue") +
  guides(size = guide_legend(title = "",
                             override.aes = list(fill = "blue")))

More about guides()

If we want to modify the size of legend items we have two choices:

• guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10))

• … or to set the sizes in the theme().

What themes are there? (I)

{ggplot2} has several built-in themes. They have several arguments for quickly customising them.

I’ve been using the default theme_gray() to change text size in charts.

msleep %>% 
  mutate(sleep_perc = sleep_total / 24,
         sleep_rem_perc = sleep_rem / 24) %>% 
  ggplot() +
  aes(x = sleep_perc,
      y = sleep_rem_perc) +
  geom_point() +
  scale_x_continuous(label = label_percent()) +
  theme_gray(base_size = 24)

What themes are there? (II)

The {ggthemes} package contains lots of really useful - and beautiful - themes.

It’s recommended that you choose a theme close to what to want and then customise it.

theme_fivethirtyeight() +
  theme(panel.grid.major = element_line(colour = "red"))

element_*() functions in legends

Most of the legend arguments expect one of these functions:

• element_line()

• element_text()

• element_rect()

Or element_black() if you want to remove a theme element.